docs: def plot_divers(self, iabscissa=1, foffset=1e-19):
          """plot fitness, sigma, axis ratio...
  
          :param iabscissa: 0 means vs evaluations, 1 means vs iterations
          :param foffset: added to f-value
  
          :See: `plot()`
  
          """
          from matplotlib.pyplot import semilogy, hold, grid, \
              axis, title, text
          fontsize = pyplot.rcParams['font.size']
  
          if not hasattr(self, 'f'):
              self.load()
          dat = self
  
          minfit = min(dat.f[:, 5])
          dfit = dat.f[:, 5] - minfit  # why not using idx?
          dfit[dfit < 1e-98] = np.NaN
  
          self._enter_plotting()
          if dat.f.shape[1] > 7:
              # semilogy(dat.f[:, iabscissa], abs(dat.f[:,[6, 7, 10, 12]])+foffset,'-k')
              semilogy(dat.f[:, iabscissa], abs(dat.f[:, [6, 7]]) + foffset, '-k')
              hold(True)
  
          # (larger indices): additional fitness data, for example constraints values
          if dat.f.shape[1] > 8:
              # dd = abs(dat.f[:,7:]) + 10*foffset
              # dd = np.where(dat.f[:,7:]==0, np.NaN, dd) # cannot be
              semilogy(dat.f[:, iabscissa], np.abs(dat.f[:, 8:]) + 10 * foffset, 'y')
              hold(True)
  
          idx = np.where(dat.f[:, 5] > 1e-98)[0]  # positive values
          semilogy(dat.f[idx, iabscissa], dat.f[idx, 5] + foffset, '.b')
          hold(True)
          grid(True)
  
  
          semilogy(dat.f[:, iabscissa], abs(dat.f[:, 5]) + foffset, '-b')
          text(dat.f[-1, iabscissa], abs(dat.f[-1, 5]) + foffset,
               r'$|f_\mathsf{best}|$', fontsize=fontsize + 2)
  
          # negative f-values, dots
          sgn = np.sign(dat.f[:, 5])
          sgn[np.abs(dat.f[:, 5]) < 1e-98] = 0
          idx = np.where(sgn < 0)[0]
          semilogy(dat.f[idx, iabscissa], abs(dat.f[idx, 5]) + foffset,
                   '.m')  # , markersize=5
  
          # lines between negative f-values
          dsgn = np.diff(sgn)
          start_idx = 1 + np.where((dsgn < 0) * (sgn[1:] < 0))[0]
          stop_idx = 1 + np.where(dsgn > 0)[0]
          if sgn[0] < 0:
              start_idx = np.concatenate(([0], start_idx))
          for istart in start_idx:
              istop = stop_idx[stop_idx > istart]
              istop = istop[0] if len(istop) else 0
              idx = xrange(istart, istop if istop else dat.f.shape[0])
              if len(idx) > 1:
                  semilogy(dat.f[idx, iabscissa], abs(dat.f[idx, 5]) + foffset,
                          'm')  # , markersize=5
              # lines between positive and negative f-values
              # TODO: the following might plot values very close to zero
              if istart > 0:  # line to the left of istart
                  semilogy(dat.f[istart-1:istart+1, iabscissa],
                           abs(dat.f[istart-1:istart+1, 5]) +
                           foffset, '--m')
              if istop:  # line to the left of istop
                  semilogy(dat.f[istop-1:istop+1, iabscissa],
                           abs(dat.f[istop-1:istop+1, 5]) +
                           foffset, '--m')
                  # mark the respective first positive values
                  semilogy(dat.f[istop, iabscissa], abs(dat.f[istop, 5]) +
                           foffset, '.b', markersize=7)
              # mark the respective first negative values
              semilogy(dat.f[istart, iabscissa], abs(dat.f[istart, 5]) +
                       foffset, '.r', markersize=7)
  
          # standard deviations std
          semilogy(dat.std[:-1, iabscissa],
                   np.vstack([list(map(max, dat.std[:-1, 5:])),
                              list(map(min, dat.std[:-1, 5:]))]).T,
                       '-m', linewidth=2)
          text(dat.std[-2, iabscissa], max(dat.std[-2, 5:]), 'max std',
               fontsize=fontsize)
          text(dat.std[-2, iabscissa], min(dat.std[-2, 5:]), 'min std',
               fontsize=fontsize)
  
          # delta-fitness in cyan
          idx = isfinite(dfit)
          if 1 < 3:
              idx_nan = np.where(idx == False)[0]  # gaps
              if not len(idx_nan):  # should never happen
                  semilogy(dat.f[:, iabscissa][idx], dfit[idx], '-c')
              else:
                  i_start = 0
                  for i_end in idx_nan:
                      if i_end > i_start:
                          semilogy(dat.f[:, iabscissa][i_start:i_end],
                                                  dfit[i_start:i_end], '-c')
                  i_start = i_end + 1
                  if len(dfit) > idx_nan[-1] + 1:
                      semilogy(dat.f[:, iabscissa][idx_nan[-1]+1:],
                                              dfit[idx_nan[-1]+1:], '-c')
              text(dat.f[idx, iabscissa][-1], dfit[idx][-1],
                   r'$f_\mathsf{best} - \min(f)$', fontsize=fontsize + 2)
  
          # overall minimum
          i = np.argmin(dat.f[:, 5])
          semilogy(dat.f[i, iabscissa], np.abs(dat.f[i, 5]), 'ro',
                   markersize=9)
          semilogy(dat.f[i, iabscissa], dfit[idx][np.argmin(dfit[idx])]
                   + 1e-98, 'ro', markersize=9)
          # semilogy(dat.f[-1, iabscissa]*np.ones(2), dat.f[-1,4]*np.ones(2), 'rd')
  
          # AR and sigma
          semilogy(dat.f[:, iabscissa], dat.f[:, 3], '-r')  # AR
          semilogy(dat.f[:, iabscissa], dat.f[:, 2], '-g')  # sigma
          text(dat.f[-1, iabscissa], dat.f[-1, 3], r'axis ratio',
               fontsize=fontsize)
          text(dat.f[-1, iabscissa], dat.f[-1, 2] / 1.5, r'$\sigma$',
               fontsize=fontsize+3)
          ax = array(axis())
          # ax[1] = max(minxend, ax[1])
          axis(ax)
          text(ax[0] + 0.01, ax[2],  # 10**(log10(ax[2])+0.05*(log10(ax[3])-log10(ax[2]))),
               '.min($f$)=' + repr(minfit))
               #'.f_recent=' + repr(dat.f[-1, 5]))
  
          # title('abs(f) (blue), f-min(f) (cyan), Sigma (green), Axis Ratio (red)')
          # title(r'blue:$\mathrm{abs}(f)$, cyan:$f - \min(f)$, green:$\sigma$, red:axis ratio',
          #       fontsize=fontsize - 0.0)
          title(r'$|f_{\mathrm{best},\mathrm{med},\mathrm{worst}}|$, $f - \min(f)$, $\sigma$, axis ratio')
  
          # if __name__ != 'cma':  # should be handled by the caller
          self._xlabel(iabscissa)
          self._finalize_plotting()
          return self